{"id":"W3213392582","doi":"10.1115/1.4052921","title":"Hydrogen Gas Refueling Infrastructure for Heavy-Duty Trucks: A Feasibility Analysis","year":2021,"lang":"en","type":"article","venue":"Journal of Energy Resources Technology","topic":"Electric Vehicles and Infrastructure","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada","funders":"","keywords":"Truck; Hydrogen vehicle; Compressed natural gas; Environmental science; Zero emission; Criteria air contaminants; Diesel fuel; Greenhouse gas; Compressed hydrogen; Environmental economics; Hydrogen fuel; Business; Waste management; Transport engineering; Hydrogen; Automotive engineering; Engineering; Hydrogen storage; Air pollutants; Air pollution; Fuel cells; Economics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001815957,0.0007160208,0.0004267876,0.001884734,0.0005994219,0.001285691,0.0009650706,0.001077344,0.004946554],"category_scores_gemma":[0.002786479,0.0004947463,0.0009308396,0.0008880592,0.0005257935,0.001829895,0.0006019687,0.0005927035,0.0003432067],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002949414,"about_ca_system_score_gemma":0.00157784,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01491936,"about_ca_topic_score_gemma":0.01092521,"domain_scores_codex":[0.9991711,0.0002825548,0.000030685,0.000102615,0.0002581701,0.0001548507],"domain_scores_gemma":[0.9969348,0.002089299,0.0001653598,0.0001149151,0.0005728488,0.0001228349],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001576366,0.00207888,0.05338335,0.0005551301,0.00008636282,0.001858461,0.0001383781,0.888705,0.01313453,0.007503265,0.0008820895,0.03009829],"study_design_scores_gemma":[0.0001439427,0.004066386,0.02129366,0.00003797996,0.0001130749,0.0001537665,0.001299568,0.9622297,0.007148249,0.002011081,0.001451918,0.0000506996],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9820587,0.00007140513,0.009793837,0.0001141264,0.000007817033,0.0004511112,0.0004714539,0.00004499554,0.006986528],"genre_scores_gemma":[0.9932963,0.00009372393,0.004840664,0.00000672282,0.000003112313,0.0001391455,0.0004013773,0.000004253646,0.001214768],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01491936,"threshold_uncertainty_score":0.02966505,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005043923451773922,"score_gpt":0.214221755509015,"score_spread":0.2091778320572411,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}